Covre optimization for image steganography by using image features

Abstract

Steganography is the art of science to hide digital media in any other digital media using different techniques. Steganography uses cover to embedded secret data, currently the cover chooses randomly, and for the same secret data every one can choose different cover without a prior knowledge which one is better, because there are no rules or measurements used for choosing suitable cover. Chooses the suitable cover is one of the major problems of steganography. This thesis develops techniques for discriminating between images which used as steganography cover in image in image steganography. Proposed algorithm is based on the hypothesis that a particular message embedding scheme leaves statistical evidence or structure that can be exploited for detection with the aid of proper selection of image features analysis. We pointed out the features of image that should be taken more seriously into account in the design of more successful steganography. This thesis suggests rules or measurements to select the proper cover for specific embedded message. It relay on the image features and also some technique to determine the expected secrecy and robustness. For each embedded image, we suggest many images to use as covers, the image features are determined for both covers and embed image. The embedded features are compared with all the covers features, and according to suggest relations in this thesis we will determine specific weight for each cover, best cover is with highest weight for that secret data. The proposed algorithm tested by using LSB image steganography, stego-image compared with the origin one which gives the promised results. Also proposed algorithm compared with other similar works and give better results.